Investigating the effects of climate change on reference evapotranspiration based on the SSP scenarios

Document Type : Research Paper

Authors

1 Department of Water Science and Engineering, Faculty of Agriculture, Bu Ali Sina University, Hamadan, Iran.

2 Lorestan University _Associate Professor Department of Water Engineering, Faculty of Agriculture and Natural Resources

3 Department of Water Science and Engineering, Faculty of Agriculture, Bu- AliSina University,Hamadan, Iran.

Abstract

 
Climate change is a phenomenon that affects many natural processes, including the hydrological cycle. Evapotranspiration is also an important part of the hydrological cycle, which is crucial in water resource management and agricultural planning. Since the estimation of evapotranspiration is always associated with uncertainties, this study examines the effects of climate change on the evapotranspiration process at the Crumbed station in Preston province. The study uses the SAP1-2.6, SAP2-4.5, SAP3-7.0, and SAP5-8.5 scenarios according to the Sixth Assessment Report (AR6) in three future time periods: near future (2023-2048), mid future (2049-2074), and far future (2075-2100). The reference evapotranspiration for the base period and future periods is calculated using the Hargreaves method. The results show that the maximum temperature at the Crumbed station will increase by an average of 0.26 to 6.3 degrees Celsius by the year 2100, compared to the base period (1988-2014). The minimum temperature will also increase by an average of 0.32 to 4.9 degrees Celsius during the same period. Additionally, the average evaporation-transpiration in all periods will increase compared to the base period. The average evaporation-transpiration in the near future will range from 4.69 to 4.82, in the mid-term future from 4.7 to 4.94, and in the far future, from 4.72 to 5.04

Keywords

Main Subjects

Investigating the effects of climate change on reference evapotranspiration based on the SSP scenarios

 

EXTENDED ABSTRACT

Introduction

Studies show that climate change can pose a threat to food security, the environment, and economic activities. It can also alter temperature and precipitation patterns in different regions, thereby affecting the hydrological cycle. Evapotranspiration is also an important part of the hydrological cycle, which holds significant importance in water resource management and agricultural planning. Therefore, studying the impact of climate change on the important parameter of reference Evapotranspiration (ETo) plays a crucial role in improving water consumption management on farms and agricultural planning.

Materials and Methods

In this study, climate data from Khorramabad station for the time range of 1988-2014 was used. Among the GCM models, the MRI-ESM2 from the Japan Meteorological Research Institute was selected as the top model due to its high resolution matching with the synoptic stations in Khorramabad. Using the climate data from this model and the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios under the sixth IPCC report (CMIP6), they were downscaled for the period of 2100-2023 for Khorramabad station. For this purpose, the most effective predictors for temperature and precipitation parameters were first identified using the Weka software, and then, using the neural network model, the monthly average values of 44 years of temperature and precipitation parameters (1970-2014) were separately used for downscaling in the observational station scale (Khorramabad station). It is worth mentioning that 75% of the observational data were used for model training and 25% of the data were used for model testing. Then, using the observational climate data and downscaled minimum and maximum temperature data, reference evaporation and transpiration were calculated for the base period and future period using the Hargreaves-Samani method.

Results

In this study, three time periods were examined: near future (2023-2048), middle (2049-2074), and far (2075-2100). For this purpose, the reference evapotranspiration was calculated for the base period and future periods using the Hargreaves method. The results showed that on average, the maximum temperature in Khorramabad station will increase by 0.26 to 6.3 degrees Celsius and the minimum temperature will increase by 0.32 to 4.9 degrees Celsius compared to the base period (1988-2014) by the year 2100. Also, the average amount of reference evapotranspiration will increase in all periods compared to the observational base period. The amount of reference evapotranspiration in the near future will vary between 4.69 to 4.82 millimeters per day, in the middle future between 4.7 to 4.94 millimeters per day, and in the far future between 4.72 to 5.04 millimeters per day, with the SSP1-2.6 scenario predicting the lowest amount and the SSP5-8.5 scenario predicting the highest amount of reference Evapotranspiration in different time periods.

Conclusion

Based on the results, it can be said that the consequences of climate change, especially in terms of temperature, are observable. According to the seasonal changes in reference evapotranspiration, the results of all SSP scenarios indicate an increase in this parameter during the cold seasons. Therefore, considering the temperature in the increase of evaporation and transpiration, we need to look for solutions for better water resource management and improve water utilization methods, especially in the agricultural sector.

References

Ahmadibaseri, N., Shirvani, A., & Nazemosadat, M. J. (2015). The Application of ANN for Downscaling GCMs Outputs for Prediction of Precipitation in Across Southern Iran. Water and Soil, 28(5), 1037-1047. (In Persian)
Akhavan, S., Ghobaei Sugh, M., & Mosaedi, A. (2016). Investigation of the effect of climate change on net irrigation requirement of main crops of Hamadan-Bahar plain using LARS-WG downscaling model. Journal of Water and Soil Conservation, 22(4), 25-46. (In Persian)
Ali N M S, Güven A. & Al-Juboori A M. (2018). Statistical Downscaling of Precipitation and Temperature Using Gene Expression Programming. Journal of Advanced Physics, 7, 518-521.
Alizadeh, Amin (2012). Principles of applied hydrology, 35th edition, Mashhad, Astan Quds Razavi Publications. (In Persian)
Alizadeh Jabehdar, A., Asadi, E. & Ghorbani, M A. (2021). Selection of the most appropriate GCM models of IPCC fourth, fifth and sixth assessment reports (Case Study: Ardabil synoptic station). Second International Conference and Fifth National Conference on Natural Resources and Environment. (In Persian)
Almazroui, M., Saeed, F., Saeed, S., Islam, MN., Ismail, M., Klutse, NAB. & Siddiqui, MH. (2020). Projected change in temperature and precipitation over Africa from CMIP6. Earth Systems and Environment, 4, 455– 475.
Ansari, S., Dehban, H., Zareian, M., & Farokhnia, A. (2022). Investigation of temperature and precipitation changes in the Iran's basins in the next 20 years based on the output of CMIP6 model. Iranian Water Researches Journal, 16(1), 11-24. (In Persian)
Arfa, A., Khashei siuki, A. & hamidianpoor, M. (2021). The effect of climate change on evapotranspiration in warm and humid conditions (Case study: South and Southeast of Iran). Journal of Rainwater Catchment Systems, 8, 37-50. (In Persian)
Aryal, A., Shrestha, S. & Babel, MS. (2019). Quantifying the sources of uncertainty in an ensemble of hydrological climate-impact projections. Theoretical and Applied Climatology, 135, 193–209.
Babolhekami, A., Gholami Sefidkouhi, M. A., & Emadi, A. (2020). The Impact of Climate Change on Reference Evapotranspiration in Mazandaran Province. Iranian Journal of Soil and Water Research, 51(2), 387-401. (In Persian)
Bhattacharya, B. & Solomatine, D. P. (2006). Machine learning in sedimentation modelling. Neural Networks, 19, 208-214.
Chen, C., Kalra A. & Ahmad, S. (2019). Hydrologic responses to climate change using downscaled GCM data on awatershed scale. Journal of Water and Climate Change, 10, 63-77.
Dibike, BY. Coulibaly, P. (2006). Temporal neural networks for downscaling climate variability and extremes. Journal of the International Neural Network Society, 19,135-144.
Dinpashoh, Y. (2006). Study of Reference Crop Evapotranspiration in I.R. of Iran. Agricultural Water Management, 84, 123-129.
Fazeli khiavi, A., Salahi, B., Goodarzi, M. (2020). Assessment effects of climate change on changes in potential evapotranspiration in the Moghan Plain by RCPs. Watershed Engineering and Management, 12, 977-993. (In Persian)
Ghorbani, M A., Deo, R C., Karimi, V., Yaseen, Z M. & Terzi, O. (2018). Implementation of a hybrid MLP-FFA model for water level prediction of Lake Egirdir, Turkey. Stochastic Environmental Research and Risk Assessment, 32, 1683-1697.
Goudarzi, M., Salahi, B. & Hosseini, S A. (2018). Estimation of Evapotranspiration Rate Due to Climate Change in the Urmia Lake Basin. Iran-Watershed Management Science & Engineering, 12, 1-12. (In Persian)
Granger, R J. (1999). Satellite-derived estimation of evapotranspiration in Gedis basin. Journal of Hydrology, 229, 70-76.
García-García, A., Cuesta-Valero, F J., Beltrami, H. & Smerdon, J E. (2019). Characterization of air and ground temperature relationships within the CMIP5 historical and future climate simulations: Journal of Geophysical Research: Atmospheres, 124, 3903-3929.
Goyal, R K. (2004). Sensitivity of evapotranspiration to global warming: a case study of arid zone of Rajasthan (India). Agricultural Water Management, 69, 1–11.
Guo, B., Zhang, J., Gong, H. & Cheng, X .(2014) .Future climate change impacts on the ecohydrology of Guishui River Basin China, Ecohydrology & Hydrobiology, 14, 55-67.
Hadi, F., Khashei Siuki, A., Shahidi, A. & Farzaneh, M R. (2016). Examination the Effect of Climate Change on Potential Evapotranspiration in Different Climates. Iranian Journal of Irrigation and Drainage, 10, 230-240. (In Persian)
Hargreaves, G H. & Samani, Z. (1985). Reference crop evapotranspiration from ambient air tempraturer. Meeting American Society of Agricultural Engineers, Chicago,12p.
Haykin, S. (1996). Neural networks expand SP's horizons. IEEE Signal Processing Magazine, 13, 24-49.
Heydari Tasheh Kaboud, S. & Khoshkhoo, Y. (2019). Projection and prediction of the annual and seasonal future reference evapotranspiration time scales in the West of Iran under RCP emission scenarios. Journal title 2019; 19 (53) :157-176. (In Persian)
Kim, J H., Sung, J H., Chung, E S., Kim, S U., Son, M. & Shiru, M S .(2021). Comparison of projection in meteorological and hydrological droughts in the Cheongmicheon Watershed for RCP4. 5 and SSP2-4.5. Sustainability, 13, 2066.
Kheyri, R., Mojarrad, F., Farhadi, B., & Masompour Samakoosh, J. (2022). Investigation of Evapotranspiration Changes of Autumn Irrigated Wheat in Iran under Climate Change Conditions. Journal of Geography and Regional Development, 20(1), 248-215. (In Persian)
Konapala, G., Mishra, A K., Wada, Y. & Mann, M. E. (2020). Climate change will affect global water availability through compounding changes in seasonal precipitation and evaporation. Nature communications, 11, 1-10.
Mehrazar, A., Massah Bavani, A., Mashal, M., & Rahimikhoob, H. (2018). Assessment of Climate Change Impacts on Agriculture of the Hashtgerd Plain with Emphasis of AR5 Models Uncertainty. Irrigation Sciences and Engineering, 41,45-59. (In Persian)
Mirhosseiny, S M R., Ghasemieh, H. & Abdollahi, K. (2021). Prediction of Monthly Potential Evapotranspiration under RCP Scenarios in Future Periods (Case Study: Golpayegan Basin), Iranian journal of Ecohydrology, 8, pp. 205-220. (In Persian)
Nikbkht Shahbazi, A. (2019). Investigation of Crop Evapotranspiration and Precipitation changes under Climate Change RCPs Scenarios in Khouzestan province. Journal of Water and Soil Conservation, 25(6), 123-139. (In Persian)
Nourani, V., Rouzegari, N., Molajou, A. & Baghanam, A H. (2020). An integrated simulation-optimization framework to optimize the reservoir operation adapted to climate change scenarios. Journal of Hydrology. 587, 125018.
O’Neill, B C., Kriegler, E., Ebi, K L., Kemp-Benedict, E., Riahi, K., Rothman, D S. & Solecki, W. (2017). The roads ahead: Narratives for shared socioeconomic pathways describing world futures in the 21st century. Global Environmental Change, 42, 169-180.
Quinlan, J R. (1992). Learning with continuous classes. In 5th Australian joint conference on artificial intelligence, Vol. 92, 343-348.
Rahman, M A., Yunsheng, L., Sultana, N. & Ongoma, V. (2018). Analysis of reference evapotranspiration (ET0) trends under climate change in Bangladesh using observed and CMIP5 data sets. Meteorology and Atmospheric Physics. 1-17.
Ramezani Etedali H, Khodabakhshi F, Kanani E. (2022). Outlook for the effects of climate change on drought according to the fifth IPCC report (case study: Ilam). Journal of Water and Soil Resources Conservation, 12, 87-107. (In Persian)
Sabziparvar, A.A. & Tabari, H. (2010). Regional estimation of reference evapotranspiration in arid and semi-arid regions. Journal of Irrigation and Drainage Engineering.
Sharafti, A., & Khazaei, M. R. (2017). Exploration of Randomness Characteristic of Rainfall Pattern Using RDP Model in Symareh Catchment. Journal of Environmental Science and Technology, 19(1), 1-14. (In Persian)
Yaghoubzadeh, M., Ahmadee, M., Boroomad Nasab, S., & Haghayghi Moghaddam, A. (2016). Impact of Climate Change on Changing Trend of Evapotranspiration during the Growth Period of Irrigated and Rainfed Field Crops by AOGCM Models. Journal of Water Research in Agriculture, 30(4), 511-523. (In Persian)
Iranian Journal of Soil and Water Research
Volume 54, Issue 11
January 2024
Pages 1759-1777

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